In this episode, we dive into Variational Autoencoders, a class of neural networks that can learn to compress data completely unsupervised! VAE's are a very hot topic right now in unsupervised modelling of latent variables and provide a unique solution to the curse of dimensionality. This video starts with a quick intro into normal autoencoders and then goes into VAE's and disentangled beta-VAE's. I aslo touch upon related topics like learning causal, latent representations, image segmentation and the reparameterization trick! Get ready for a pretty technical episode! Paper references: - Disentangled VAE's (DeepMind 2016): https://arxiv.org/abs/1606.05579 - Applying disentangled VAE's to RL: DARLA (DeepMind 2017): https://arxiv.org/abs/1707.08475 - Original VAE paper (2013): https://arxiv.org/abs/1312.6114 If you want to support this channel, here is my patreon link: https://patreon.com/ArxivInsights --- You are amazing!! ;) If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: https://pensight.com/x/xander-steenbrugge